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Record W4206186093 · doi:10.17762/de.vol2022iss1.8728

Fault-Tolerant Back-to-Back Converter for Direct-Drive Permanent Magnet Synchronous Generators Wind Turbines Using Direct Torque and Power Control Techniques

2022· article· en· W4206186093 on OpenAlexvenueno aff
M. Anusha

Bibliographic record

VenueDesign Engineering · 2022
Typearticle
Languageen
FieldEngineering
TopicMultilevel Inverters and Converters
Canadian institutionsnot available
Fundersnot available
KeywordsFault toleranceFault (geology)Permanent magnet synchronous generatorWind powerDependabilityAlternatorEngineeringComputer scienceElectrical engineeringPower (physics)Automotive engineeringMagnetReliability engineering

Abstract

fetched live from OpenAlex

Fault-tolerance is considered critical in wind turbines to improve their dependability and availability. This the document has a fault-tolerant permanent magnet with direct drive new direct control synchronous generator PMSM methods capable of handling high-power semiconductors OPEN-CIRCUIT failure in back-to-back transmissions. The faults-tolerance scheme is made up of 5 legs. A converter through an interlinked leg linked to an alternator phase and a converter with a shared leg connected with an alternator phase, a converter through a Triode for Alternating Current to its appropriate grid phase. The creation of DTC and DPC techniques for MSC and GSC is the major contribution of this work. A dependable failure diagnostics algorithm is also implemented, which provides the information needed to instantly activate fault-tolerant remedial techniques without the use of extra sensors. To validate the efficacy of the proposed fault-tolerant PERMANENT MAGNET SYNCHRONOUS GENERATORS drive, simulation and experimental data are provided.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.012
GPT teacher head0.199
Teacher spread0.188 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2022
Admission routes1
Has abstractyes

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